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Use a single u-law embedding

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This commit is contained in:
Jean-Marc Valin 2019-01-21 16:52:57 -05:00
parent dc082d7c1c
commit b84a06dd08
4 changed files with 14 additions and 18 deletions
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11
dnn/test_lpcnet.py
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@ -63,13 +63,12 @@ periods = (.1 + 50*features[:,:,36:37]+100).astype('int16')
model.load_weights('lpcnet20c_384_10_G16_80.h5')
model.load_weights('lpcnet20g_384_10_G16_02.h5')
order = 16
pcm = np.zeros((nb_frames*pcm_chunk_size, ))
fexc = np.zeros((1, 1, 2), dtype='float32')
iexc = np.zeros((1, 1, 1), dtype='int16')
fexc = np.zeros((1, 1, 3), dtype='int16')
state1 = np.zeros((1, model.rnn_units1), dtype='float32')
state2 = np.zeros((1, model.rnn_units2), dtype='float32')
@ -88,7 +87,7 @@ for c in range(0, nb_frames):
pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1])
fexc[0, 0, 1] = lin2ulaw(pred)
p, state1, state2 = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state1, state2])
p, state1, state2 = dec.predict([fexc, cfeat[:, fr:fr+1, :], state1, state2])
#Lower the temperature for voiced frames to reduce noisiness
p *= np.power(p, np.maximum(0, 1.5*features[c, fr, 37] - .5))
p = p/(1e-18 + np.sum(p))
@ -96,8 +95,8 @@ for c in range(0, nb_frames):
p = np.maximum(p-0.002, 0).astype('float64')
p = p/(1e-8 + np.sum(p))
iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))
pcm[f*frame_size + i] = pred + ulaw2lin(iexc[0, 0, 0])
fexc[0, 0, 2] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))
pcm[f*frame_size + i] = pred + ulaw2lin(fexc[0, 0, 2])
fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i])
mem = coef*mem + pcm[f*frame_size + i]
#print(mem)